Energy poverty from a life cycle sustainability assessment perspective
Bibliographic record
Abstract
Energy poverty (EP) and energy security (ES) are complex, multi-dimensional challenges with profound environmental, economic, and social implications that persist in both developed and developing nations. Addressing EP requires a holistic, life-cycle perspective to prevent unintended consequences, consider problem-shifting and sub-optimization, while managing trade-offs for sustainable ES. However, despite numerous proposed solutions, a comprehensive triple-bottom-line framework that integrates a life-cycle perspective remains absent in EP decision-making. Life cycle sustainability assessment (LCSA) offers a powerful methodology for addressing EP by encompassing all sustainability dimensions needed to eradicate it. This study conducts a comprehensive review of EP determinants and establishes a novel mapping between LCSA impact categories and EP drivers. Findings reveal that affordability, accessibility, and emissions are fundamental to EP/ES, with demographics and regional disparities influencing vulnerability. The mapping highlights primary determinants of EP/ES, including fair salary, poverty alleviation, public commitment to sustainability issues, climate change, and land use. To enhance the applicability of the LCSA framework to EP/ES, new categories related to energy and consumption are introduced, such as ‘education provided online’, ‘policy development and implementation’, and ‘subsidization’, which capture critical nuances of EP solutions. Additionally, identified gaps in LCSA methodology offer new insights for mitigating EP, strengthening ES, and refining LCSA itself for broader sustainability applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".